Multi-level interpretable logic tree analysis: A data-driven approach for hierarchical causality analysis

Waghen, Kerelous; Ouali, Mohamed-Salah

doi:10.1016/j.eswa.2021.115035

Cited by 16 publications

(11 citation statements)

References 33 publications

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“…This concept consists of helping the optimization process by injecting additional knowledge. One way to address this is by searching out for patterns (e.g., Waghen & Ouali [20]) in the failure data set that enables identifying parts of the FT and then assemble them.…”

Section: Discussionmentioning

confidence: 99%

“…This algorithm also manages to infer FT models with VoT gates. In Waghen & Ouali [20] the authors further extended their work to the Multi-level Interpretable Logic Tree Analysis (MILTA), which tackles the problem of multiple cause-and-effect sequences (the latter a limitation of their previous version, the ILTA algorithm) by incorporating Bayesian probability rules.…”

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confidence: 99%

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Automatic inference of fault tree models via multi-objective evolutionary algorithms

Jimenez-Roa¹,

Heskes²,

Tinga³

et al. 2022

Preprint

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Fault tree analysis is a well-known technique in reliability engineering and risk assessment, which supports decision-making processes and the management of complex systems. Traditionally, fault tree (FT) models are built manually together with domain experts, considered a time-consuming process prone to human errors. With Industry 4.0, there is an increasing availability of inspection and monitoring data, making techniques that enable knowledge extraction from large data sets relevant. Thus, our goal with this work is to propose a data-driven approach to infer efficient FT structures that achieve a complete representation of the failure mechanisms contained in the failure data set without human intervention. Our algorithm, the FT-MOEA, based on multi-objective evolutionary algorithms, enables the simultaneous optimization of different relevant metrics such as the FT size, the error computed based on the failure data set and the Minimal Cut Sets. Our results show that, for six case studies from the literature, our approach successfully achieved automatic, efficient, and consistent inference of the associated FT models. We also present the results of a parametric analysis that tests our algorithm for different relevant conditions that influence its performance, as well as an overview of the data-driven methods used to automatically infer FT models.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Automatic inference of fault tree models via multi-objective evolutionary algorithms

Jimenez-Roa¹,

Heskes²,

Tinga³

et al. 2022

Preprint

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“…(1) Establishing a Fault Tree Model Deductive analysis is used to create a fault tree. The top event is selected and the cause of the issue is then classified into intermediate events [4] . The basic events that do not require decomposition are then regarded as the base event.…”

Section: Fault Tree Analysismentioning

confidence: 99%

Fault analysis and impact weight calculation of marine diesel engine unable to start based on fault tree and analytic hierarchy process

Li,

Yang,

Hou

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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In response to the malfunction of "marine diesel engine unable to start", the possible causes of the malfunction were deduced and a fault tree model was established. On this basis, the fault tree model was transformed into a hierarchical analysis model, and the target layer, criterion layer, and solution layer were constructed. Through expert consultation and data collection, a judgment matrix for each layer was constructed, and the effectiveness of the judgment matrix was verified. The impact weights of each base event on the top event were determined, and targeted suggestions were proposed for the management of marine diesel engine systems.

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“…While most data-driven approaches only require information about basic events, LIFT also needs information about failures of intermediate events. Both the ILTA [27] and MILTA [28] algorithms make use of Knowledge Discovery in Data sets, Interpretable Logic Tree Analysis, and Bayesian probability rules. The method in [15] first learns a Bayesian Network and then translates it into an FT model, using blacklists and whitelists to define missing or present arcs.…”

Section: Relatedmentioning

confidence: 99%

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Jimenez-Roa

Volk

Stoelinga

2022

Lecture Notes in Computer Science

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We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes. This research has been partially funded by NWO under the grant PrimaVera number NWA.1160.18.238 and by the ERC Consolidator grant CAESAR number 864075.

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Multi-level interpretable logic tree analysis: A data-driven approach for hierarchical causality analysis

Cited by 16 publications

References 33 publications

Automatic inference of fault tree models via multi-objective evolutionary algorithms

Automatic inference of fault tree models via multi-objective evolutionary algorithms

Fault analysis and impact weight calculation of marine diesel engine unable to start based on fault tree and analytic hierarchy process

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

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